Sort prompts + add doc for prompts

api/requirements.py

@@ -46,7 +46,7 @@ async def categorize_reqs(params: ReqGroupingRequest, prompt_env: Environment =
     messages = []
 
     # categorize the requirements using their indices
-    req_prompt = await prompt_env.get_template("classify.txt").render_async(**{
+    req_prompt = await prompt_env.get_template("public/classify.txt").render_async(**{
         "requirements": [rq.model_dump() for rq in params.requirements],
         "max_n_categories": params.max_n_categories,
         "response_schema": _ReqGroupingOutput.model_json_schema()})

api/solutions.py

@@ -45,7 +45,7 @@ async def bootstrap_solutions(req: SolutionBootstrapRequest, prompt_env: Environ
         fmt_completion = await llm_router.acompletion("gemini-v2", messages=[
             {
                 "role": "user",
-                "content": await prompt_env.get_template("format_requirements.txt").render_async(**{
+                "content": await prompt_env.get_template("public/format_requirements.txt").render_async(**{
                     "category": cat.model_dump(),
                     "response_schema": InsightFinderConstraintsList.model_json_schema()
                 })
@@ -66,7 +66,7 @@ async def bootstrap_solutions(req: SolutionBootstrapRequest, prompt_env: Environ
 
         format_solution = await llm_router.acompletion("gemini-v2", messages=[{
             "role": "user",
-            "content": await prompt_env.get_template("bootstrap_solution.txt").render_async(**{
+            "content": await prompt_env.get_template("public/bootstrap_solution.txt").render_async(**{
                 "category": cat.model_dump(),
                 "technologies": technologies.model_dump()["technologies"],
                 "user_constraints": req.user_constraints,
@@ -103,7 +103,7 @@ async def criticize_solution(params: CriticizeSolutionsRequest, prompt_env: Envi
     """Criticize the challenges, weaknesses and limitations of the provided solutions."""
 
     async def __criticize_single(solution: SolutionModel):
-        req_prompt = await prompt_env.get_template("criticize.txt").render_async(**{
+        req_prompt = await prompt_env.get_template("public/criticize.txt").render_async(**{
             "solutions": [solution.model_dump()],
             "response_schema": _SolutionCriticismOutput.model_json_schema()
         })
@@ -131,7 +131,7 @@ async def refine_solutions(params: CritiqueResponse, prompt_env: Environment = D
     """Refines the previously critiqued solutions."""
 
     async def __refine_solution(crit: SolutionCriticism):
-        req_prompt = await prompt_env.get_template("refine_solution.txt").render_async(**{
+        req_prompt = await prompt_env.get_template("public/refine_solution.txt").render_async(**{
             "solution": crit.solution.model_dump(),
             "criticism": crit.criticism,
             "response_schema": _RefinedSolutionModel.model_json_schema(),

prompts/private/README

@@ -1 +1,8 @@
-Prompts for client-side inference (in `private/`) should only use basic variable templating.
+>**!!WARNING!!**:  Prompts for client-side inference (in `private/`) should only use basic variable templating.
+
+## List of client-side prompts used for assessment using private LLM
+- `assess` : Prompt for assessing the user idea
+- `extract` : Prompt for extracting information from the LLM assessment in a structured way
+- `refine` : Prompt for refining the user idea with supplied insights
+- `fto_topics` : Retrieves from a user idea the topics to look up for FTO analysis
+- `fto_assess` : Assess the FTO analysis results

prompts/public/README.md

@@ -0,0 +1,9 @@
+
+## List of public prompts
+- `format_requirements`: Glue prompt for extracting most important constraints to retrieve relevant technologies
+- `bootstrap_solution` : Boostrap a solution from a list of technologies
+- `structure_solution` : Format a bootstrapped solution into a structured outptut
+- `criticize`: Criticize a boostraped solution for refinement
+- `refine_solution` : Refines a boostrapped solution using critics
+- `classify` : Categorize requirements into sections
+- 

prompts/structure_solution.txt

@@ -1,12 +0,0 @@
-<role>You are an expert system designer</role>
-<task>Your task is to take a solution you've created previously and structure it into a JSON object.</task>
-
-Here is the solution
-<solution>
-{{solution}}
-</solution>
-
-<response_format>
-Reply in JSON using the following schema:
-{{response_schema}}
-</response_format>